An electromigration failure data set can include a number of electromigration failure data points, which typically indicate a probability of failure and a time to failure. A number of known failure modes, i.e., types of electromigration failures, can exist within an electromigration failure data set and each data point can be associated with a single failure mode. However, the association between each data point and each failure mode existing in the electromigration failure data set is typically unknown when the electromigration failure data set is acquired.
One conventional method for associating each data point to each failure mode includes determining a single log-normal distribution fit for all of the data points associated with multiple failure modes. The single log-normal distribution fit is determined by grouping the data points into a number of groups equal to the number of known failures and determining a log-normal distribution fit for each group of data points. Each log-normal distribution fit is then weighted based on a probability of all data points to determine a single log-normal distribution fit. However, since the single log-normal distribution fit is based on a probability of all data points and consequently all failure modes, the association between each data point and each failure mode made using the conventional method may be inaccurate.
Accordingly, there exists a strong need in the art for a method that can accurately determine the association between each data point in an electromigration failure data set and each failure mode therein.